Under Review

Rethinking probabilistic prediction: lessons learned from the 2016 U.S. presidential election

Interpretation of probability plausibility prediction statistical modeling validity

Cite as:

Harry Crane and Ryan Martin (2018). Rethinking probabilistic prediction: lessons learned from the 2016 U.S. presidential election. RESEARCHERS.ONE, https://www.researchers.one/article/2018-08-12.

Abstract:

Whether the predictions put forth prior to the 2016 U.S. presidential election were right or wrong is a question that led to much debate. But rather than focusing on right or wrong, we analyze the 2016 predictions with respect to a core set of {\em effectiveness principles}, and conclude that they were ineffective in conveying the uncertainty behind their assessments. Along the way, we extract key insights that will help to avoid, in future elections, the systematic errors that lead to overly precise and overconfident predictions in 2016. Specifically, we highlight shortcomings of the classical interpretations of probability and its communication in the form of predictions, and present an alternative approach with two important features.  First, our recommended predictions are safer in that they come with certain guarantees on the probability of an erroneous prediction; second, our approach easily and naturally reflects the (possibly substantial) uncertainty about the model by outputting plausibilities instead of probabilities.